How to use the omegaml.util.reshaped function in omegaml

def partial_fit(
            self, modelname, Xname, Yname=None, pure_python=True, **kwargs):
        model = self.model_store.get(modelname)
        X, metaX = self.data_store.get(Xname), self.data_store.metadata(Xname)
        Y, metaY = None, None
        if Yname:
            Y, metaY = (self.data_store.get(Yname),
                        self.data_store.metadata(Yname))
        model.partial_fit(reshaped(X), reshaped(Y), **kwargs)
        # store information required for retraining
        model_attrs = {
            'metaX': metaX.to_mongo(),
            'metaY': metaY.to_mongo() if metaY is not None else None,
        }
        try:
            import sklearn
            model_attrs['scikit-learn'] = sklearn.__version__
        except:
            model_attrs['scikit-learn'] = 'unknown'
        meta = self.model_store.put(model, modelname, attributes=model_attrs)
        return meta

def fit(self, modelname, Xname, Yname=None, pure_python=True, **kwargs):
        model = self.model_store.get(modelname)
        X, metaX = self.data_store.get(Xname), self.data_store.metadata(Xname)
        Y, metaY = None, None
        if Yname:
            Y, metaY = (self.data_store.get(Yname),
                        self.data_store.metadata(Yname))
        model.fit(reshaped(X), reshaped(Y), **kwargs)
        # store information required for retraining
        model_attrs = {
            'metaX': metaX.to_mongo(),
            'metaY': metaY.to_mongo() if metaY is not None else None,
        }
        try:
            import sklearn
            model_attrs['scikit-learn'] = sklearn.__version__
        except:
            model_attrs['scikit-learn'] = 'unknown'
        meta = self.model_store.put(model, modelname, attributes=model_attrs)
        return meta

def score(
            self, modelname, Xname, Yname, rName=None, pure_python=True,
            **kwargs):
        model = self.model_store.get(modelname)
        X = self.data_store.get(Xname)
        Y = self.data_store.get(Yname)
        result = model.score(reshaped(X), reshaped(Y), **kwargs)
        if rName:
            meta = self.model_store.put(result, rName)
            result = meta
        return result

def predict_proba(
            self, modelname, Xname, rName=None, pure_python=True, **kwargs):
        data = self.data_store.get(Xname)
        model = self.model_store.get(modelname)
        result = model.predict_proba(reshaped(data), **kwargs)
        if pure_python:
            result = result.tolist()
        if rName:
            meta = self.data_store.put(result, rName)
            result = meta
        return result

def decision_function(self, modelname, Xname, rName=None, pure_python=True, **kwargs):
        model = self.model_store.get(modelname)
        X = self.data_store.get(Xname)
        result = model.decision_function(reshaped(X), **kwargs)
        if pure_python:
            result = result.tolist()
        if rName:
            meta = self.data_store.put(result, rName)
            result = meta
        return result

def fit_transform(
            self, modelname, Xname, Yname=None, rName=None, pure_python=True,
            **kwargs):
        model = self.model_store.get(modelname)
        X, metaX = self.data_store.get(Xname), self.data_store.metadata(Xname)
        Y, metaY = None, None
        if Yname:
            Y, metaY = (self.data_store.get(Yname),
                        self.data_store.metadata(Yname))
        result = model.fit_transform(reshaped(X), reshaped(Y), **kwargs)
        # store information required for retraining
        model_attrs = {
            'metaX': metaX.to_mongo(),
            'metaY': metaY.to_mongo() if metaY is not None else None
        }
        try:
            import sklearn
            model_attrs['scikit-learn'] = sklearn.__version__
        except:
            model_attrs['scikit-learn'] = 'unknown'
        meta = self.model_store.put(model, modelname, attributes=model_attrs)
        if pure_python:
            result = result.tolist()
        if rName:
            meta = self.data_store.put(result, rName)
        result = meta

def predict(
            self, modelname, Xname, rName=None, pure_python=True, **kwargs):
        data = self.data_store.get(Xname)
        model = self.model_store.get(modelname)
        result = model.predict(reshaped(data), **kwargs)
        if pure_python:
            result = result.tolist()
        if rName:
            meta = self.data_store.put(result, rName)
            result = meta
        return result